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Procountor MCP Server and API by Cotlet

Create receipt in Procountor

procountor_create_receipt
Read-onlyIdempotent

Create receipt in the user's own Procountor account from ChatGPT, Claude or any AI agent. Returns how to do it in Procountor step by step (screens, buttons, fields) and opens nothing by itself; then do it with the cotlet_browser_* tools in the user's Procountor Cloud Browser. Procountor's official API: Yes, Procountor has a REST API with write access to invoices, ledger receipts and business partners, but you cannot just switch it on (checked 14 Oct 2026).

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.6/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description is consistent with the annotations (readOnlyHint, idempotentHint, destructiveHint=false) by confirming nothing is opened or changed, and it discloses the return content (screens, buttons, fields). Beyond that it adds little behavioral detail — no note on how the user's Procountor account is identified, whether authentication or an active session is required, or how long the returned steps stay valid.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The core behavior is front-loaded, but the final paragraph about Procountor's REST API, its write access, and the 'checked 14 Oct 2026' date is tangential for a zero-parameter instruction tool and dilutes the message. The key caveat ('opens nothing by itself') is buried mid-sentence.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter, no-output-schema tool the description covers what is returned and what to do next, which is the essential loop. It is missing the implicit prerequisites — account/session context and confirmation that no arguments are needed — that an agent would need to invoke it confidently rather than guess at required inputs.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes no parameters, so the baseline applies and the schema needs no elaboration. The description could have usefully explained how the target account/company is determined without any input, but a 4 is appropriate given the zero-parameter schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a concrete outcome: it returns step-by-step instructions (screens, buttons, fields) for creating a receipt in the user's Procountor account, and explicitly says it 'opens nothing by itself'. This distinguishes it from the mutation-style siblings (procountor_create_invoice) and from the raw browser tools, though the name 'create_receipt' still implies a write action the tool does not perform.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives clear usage context: call this first, then execute the steps with the cotlet_browser_* tools in Procountor Cloud Browser. It does not, however, explicitly rule out cotlet_app_guide or say when a different app guide path is preferable, so the alternative mapping is only implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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